Is Data Analytics Ushering in the Modern Age of Weather Forecasting?

Data analytics has become very useful in the weather forecasting profession in recent years.
big data and weather forecasting
Shutterstock Photo License - By ekapol sirachainan

Weather forecasting technology has grown from strength to strength in the last few decades. Gone are the days when you had to wait for the local news channel to share the weather forecasts for the next day. Nor do you have to reach out to the newspaper to find out what the weather is going to be like throughout the day.

Instead, you’ve got access to a broad spectrum of valuable weather data right at your fingertips. As long as a user is connected to the internet, they can check the current weather, as well as 7-day or 14-day predictions using their smartphone or computer. These data-driven predictions also tend to be surprisingly accurate.

So, what’s behind the stellar transformation of weather technology?

Simply put, it involves a diverse array of tech innovations, from artificial intelligence and machine learning to the internet of things (IoT) and wireless communication networks. But if there’s one technology that has revolutionized weather forecasting, it has to be data analytics.

In this blog, we’ll delve deeper into the impact of data analytics on weather forecasting and find out whether it’s worth the hype. But let’s first understand how new-age weather intelligence platforms work.

Weather Forecasting in 2021: A Closer Look

Modern weather forecasting apps and websites are built using robust application programming interfaces (APIs). Whether you’re using a specialized app that’s been specifically designed for your industry or a generic weather app, it uses an API to access weather data.

For instance, Tomorrow’s weather API retrieves crucial weather data, such as temperature, precipitation, air quality index, pollen index, etc., from various sources. Also, it extracts historical weather data from various databases.

Any app that uses Tomorrow’s weather API gets access to all this powerful data in real-time. This, in turn, means users get access to accurate and the most recent weather updates.

But having access to weather-related information isn’t enough. You still need a mechanism to make sense of the huge volumes of data gathered from various sources, such as satellites and radar stations.

That’s where data analytics steps into the picture. Let’s explore how it improves the accuracy and efficiency of weather forecasting.

Big Data Analytics & Weather Forecasting: Understanding the Connection

Big data analytics refers to a combination of technologies used to derive actionable insights from massive amounts of data. It’s particularly useful for weather forecasting because the latter involves huge volumes of data, as well as too many variables.

Traditionally, weather forecasting platforms would depend on the expertise and intuition of meteorologists to derive predictions about future weather. Needless to say, that process was inefficient and time-consuming. Also, it was vulnerable to human errors and failed to generate precise forecasts for specific locations.

Data analytics uses AI and ML to automate the process of collecting and evaluating weather data to extract relevant insights. It’s faster and more accurate. Also, it eliminates the likelihood of human error and helps weather intelligence platforms generate more specific forecasts.

Hyperlocal Weather Forecasts Made Easy

That means you can now learn about the weather conditions at precise locations, such as residential buildings, airports, farms, construction sites, etc. Combined with IoT, it has propelled the rise of hyperlocal weather forecasting. Hadoop has also helped considerably with weather forecasting.

Hyperlocal weather intelligence platforms gather weather data from various on-ground sources, such as smartphones, CCTV cameras, smart bins, connected cars, etc. The data collected from these devices is analyzed to predict the weather at a particular location.

Hyperlocal forecasts come in handy for a wide array of industries, including agriculture, healthcare, aviation, facility management, and event planning.

Real-Time Weather Insights

Data analytics eliminates the need to wait for intuition-based weather predictions from a meteorologist. Instead, it uses AI-powered algorithms to process weather data and generates real-time weather forecasts.

That means businesses can get access to valuable insights about what turn the weather will take in the next hour, day, and week. This, in turn, helps organizations optimize staffing and resource allocation, as well as maintain business continuity in case of inclement weather. Also, it helps them ensure the safety of workers and employees.

Additionally, meteorologists can use data analytics to better monitor and predict the course of extreme weather events, such as cyclones and storms. They can precisely predict when and where a storm will make landfall. It’s proving to be a game-changer in disaster management and recovery.

Harness the Potential of Data Analytics for Weather Forecasting

Accurate, precise, and timely weather data is integral to the survival of modern businesses. Use a feature-packed weather API to build a robust weather intelligence platform that’s tailored to your needs. Leverage automation and big data analytics to process weather data and generate accurate, hyperlocal forecasts in real-time.

Dariia Herasymova
Dariia Herasymova is a Recruitment Team Lead at Devox Software. She hires software development teams for startups, small businesses, and enterprises. She carries out a full cycle of recruitment; creates job descriptions based on talks with clients, searches and interviews candidates, and onboards the newcomers. Dariia knows how to build HR and recruitment processes from scratch. She strives to find a person with appropriate technical and soft skills who will share the company's values. When she has free time, she writes articles on various outsourcing models for our blog.